{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual Pipelines \n",
    "\n",
    "This notebook demonstrates a proof of concept for a visual pipeline for analytics. \n",
    "\n",
    "![Yellowbrick Prototype Pipeline Objects](figures/pipeline_prototype.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import sys \n",
    "\n",
    "# Modify the path \n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yellowbrick as yb \n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets \n",
    "\n",
    "Note that if datasets do not exist, please see the `download.py` located in this directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FIXTURES  = os.path.join(os.getcwd(), \"data\")\n",
    "credit    = pd.read_excel(os.path.join(FIXTURES, \"credit.xls\"), header=1)\n",
    "concrete  = pd.read_excel(os.path.join(FIXTURES, \"concrete.xls\"))\n",
    "occupancy = pd.read_csv(os.path.join('data','occupancy','datatraining.txt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rename the columns of the datasets for ease of use. \n",
    "credit.columns = [\n",
    "    'id', 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n",
    "    'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n",
    "    'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n",
    "    'jul_pay', 'aug_pay', 'sep_pay', 'default'\n",
    "]\n",
    "\n",
    "concrete.columns = [\n",
    "    'cement', 'slag', 'ash', 'water', 'splast',\n",
    "    'coarse', 'fine', 'age', 'strength'\n",
    "]\n",
    "\n",
    "occupancy.columns = [\n",
    "    'date', 'temp', 'humid', 'light', 'co2', 'hratio', 'occupied'\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Visualization Prototype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "class VisualPipeline(Pipeline):\n",
    "    \n",
    "    def draw(self):\n",
    "        \"\"\"\n",
    "        Calls the draw method on every visual transformer/estimator  \n",
    "        \"\"\"\n",
    "        for name, estimator in self.steps:\n",
    "            try:\n",
    "                estimator.draw()\n",
    "            except AttributeError:\n",
    "                continue \n",
    "\n",
    "                \n",
    "class ClassifierEvaluation(object):\n",
    "    \n",
    "    def draw(self):\n",
    "        yb.crplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       " ('model', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "       intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "       multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "       verbose=0))]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "model = Pipeline([\n",
    "    ('scale', StandardScaler()), \n",
    "    ('model', LinearSVC())\n",
    "])\n",
    "\n",
    "model.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Visualizer` class\n",
    "\n",
    "In base.py at the root of yellowbrick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Visualizer(BaseEstimator):\n",
    "    \"\"\"\n",
    "    Base class for feature visualization and model visualization.\n",
    "    \n",
    "    Ensures that styling arguments are passed in.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, **kwargs):\n",
    "        self.size  = kwargs.pop('size')\n",
    "        self.color = kwargs.pop('color')\n",
    "        \n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        \"\"\"\n",
    "        Fits a transformer to X and y \n",
    "        \"\"\"\n",
    "        pass\n",
    "    \n",
    "    def draw(self, **kwargs):\n",
    "        pass\n",
    "    \n",
    "    def show(self, model=None):\n",
    "        \"\"\"\n",
    "        Visualizes either data features \n",
    "        or fitted model scores\n",
    "        \"\"\"\n",
    "        pass\n",
    "    \n",
    "    def fitdraw(self, X, y=None):\n",
    "        \"\"\"\n",
    "        Fits a transformer to X and y then returns\n",
    "        visualization of features or fitted model.\n",
    "        \"\"\"\n",
    "        pass\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `FeatureVisualizer` base class\n",
    "\n",
    "Comes between or at the end of transformers but before the estimator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class FeatureVisualizer(Visualizer, TransformerMixin):\n",
    "    \"\"\"\n",
    "    Base class for feature visualization to investigate features \n",
    "    individually or together.\n",
    "    \n",
    "    FeatureVisualizer is itself a transformer so that it can be used in\n",
    "    a Scikit-Learn Pipeline to perform automatic visual analysis during build.\n",
    "    \n",
    "    Accepts as input a DataFrame or Numpy array.\n",
    "    \"\"\"    \n",
    "    \n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        pass\n",
    "    \n",
    "    def transform(self, X):\n",
    "        pass\n",
    "    \n",
    "    def show(self, data=None):\n",
    "        \"\"\"\n",
    "        Visualize data features individually or together\n",
    "        \"\"\"\n",
    "        pass\n",
    "    \n",
    "    def fitdraw(self, X, data=None):\n",
    "        pass\n",
    "    \n",
    "    def fit_transform(self, X, data=None):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `ScoreVisualizer` class for estimators\n",
    "\n",
    "Base class to follow an estimator in a pipeline. Not a transformer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ScoreVisualizer(Visualizer):\n",
    "    \"\"\"\n",
    "    Base class to follow an estimator in a visual pipeline.\n",
    "    \n",
    "    Draws the score for the fitted model.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, model):\n",
    "        pass\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    \n",
    "    def predict(self, X):\n",
    "        pass\n",
    "    \n",
    "    def score(self, y, y_pred=None):\n",
    "        \"\"\"\n",
    "        Score will call draw to visualize model performance.\n",
    "        If y_pred is None, call fit-predict on the model to get a y_pred.\n",
    "        \"\"\"\n",
    "        return self.draw(y,y_pred)\n",
    "\n",
    "    def draw(self, X, y):\n",
    "        pass \n",
    "    \n",
    "    def show(self, **kwargs):\n",
    "        \"\"\"\n",
    "        The user calls show\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from yellowbrick.colors import ddlheatmap\n",
    "from yellowbrick.exceptions import YellowbrickTypeError\n",
    "from yellowbrick.utils import get_model_name, isestimator, isclassifier\n",
    "from sklearn.metrics import auc, roc_auc_score, roc_curve, precision_recall_fscore_support\n",
    "\n",
    "class ClassificationScoreVisualizer(ScoreVisualizer):\n",
    "    \n",
    "    def __init__(self, model):\n",
    "        \"\"\"\n",
    "        Check to see if model is an instance of a classifer.\n",
    "        Should return a metrics mismatch error if it isn't.\n",
    "        \"\"\"\n",
    "        if not isclassifier(model):\n",
    "            raise YellowbrickTypeError(\n",
    "                \"This estimator is not a classifier; try a regression or clustering score visualizer instead!\"\n",
    "        )\n",
    "\n",
    "class ROCAUC(ClassificationScoreVisualizer):\n",
    "    \"\"\"\n",
    "    Plot the ROC to visualize the tradeoff between the classifier's\n",
    "    sensitivity and specificity.\n",
    "    \"\"\"\n",
    "    def __init__(self, model, **kwargs):\n",
    "        \"\"\"\n",
    "        Pass in a model to generate a ROC curve.\n",
    "        \"\"\"\n",
    "        self.estimator = model\n",
    "        self.name = get_model_name(self.estimator)\n",
    "        super(ROCAUC, self).__init__(model, **kwargs)\n",
    "        self.colors = {\n",
    "            'roc': kwargs.pop('roc_color', '#2B94E9'),\n",
    "            'diagonal': kwargs.pop('diagonal_color', '#666666'),\n",
    "        }\n",
    "        plt.figure()  # save to object?\n",
    "        \n",
    "    def fit(self):\n",
    "        pass\n",
    "    \n",
    "    def predict(self):\n",
    "        pass\n",
    "    \n",
    "    def score(self, y, y_pred=None):\n",
    "        self.fpr, self.tpr, self.thresholds = roc_curve(y, y_pred)\n",
    "        self.roc_auc = auc(self.fpr, self.tpr)\n",
    "        self.draw(y, y_pred)\n",
    "        \n",
    "    def draw(self, y, y_pred): # don't need y and y_pred here\n",
    "        \"\"\"\n",
    "        Renders ROC-AUC plot.\n",
    "        Called internally by score, possibly more than once\n",
    "        \"\"\"  \n",
    "        plt.plot(self.fpr, self.tpr, c=self.colors['roc'], label='AUC = {:0.2f}'.format(self.roc_auc))\n",
    "\n",
    "        # Plot the line of no discrimination to compare the curve to.\n",
    "        plt.plot([0,1],[0,1],'m--',c=self.colors['diagonal'])\n",
    "\n",
    "\n",
    "    def show(self, **kwargs):\n",
    "        \"\"\"        \n",
    "        Called by user.\n",
    "        \n",
    "        Only takes self.\n",
    "        \n",
    "        Take in the model as input and generates a plot of\n",
    "        the ROC plots with AUC metrics embedded.\n",
    "        \"\"\"\n",
    "        plt.title('ROC for {}'.format(self.name))\n",
    "        plt.legend(loc='lower right')\n",
    "\n",
    "        plt.xlim([-0.02,1])\n",
    "        plt.ylim([0,1.1])\n",
    "\n",
    "        return plt\n",
    "\n",
    "class ClassificationReport(ClassificationScoreVisualizer):\n",
    "    \"\"\"\n",
    "    Classification report that shows the precision, recall, and F1 scores\n",
    "    for the model. Integrates numerical scores as well color-coded heatmap.\n",
    "    \"\"\"\n",
    "    def __init__(self, model, **kwargs):\n",
    "        \"\"\"\n",
    "        Pass in a fitted model to generate a ROC curve.\n",
    "        \"\"\"\n",
    "        self.estimator = model\n",
    "        self.name = get_model_name(self.estimator)\n",
    "        self.cmap = kwargs.pop('cmap', ddlheatmap)\n",
    "        self.classes = model.classes_\n",
    "\n",
    "        \n",
    "    def score(self, y, y_pred=None, **kwargs):\n",
    "        \"\"\"\n",
    "        Generates the Scikit-Learn classification_report\n",
    "        \"\"\"\n",
    "        self.keys = ('precision', 'recall', 'f1')\n",
    "        self.scores = precision_recall_fscore_support(y, y_pred, labels=self.classes)\n",
    "        self.scores = map(lambda s: dict(zip(self.classes, s)), self.scores[0:3])\n",
    "        self.scores = dict(zip(self.keys, self.scores))  \n",
    "        self.draw(y, y_pred)\n",
    "\n",
    "        \n",
    "    def draw(self, y, y_pred):\n",
    "        \"\"\"\n",
    "        Renders the classification report across each axis.\n",
    "        \"\"\"        \n",
    "        fig, ax = plt.subplots(1)\n",
    "\n",
    "        self.matrix = []\n",
    "        for cls in self.classes:\n",
    "            self.matrix.append([self.scores['precision'][cls],self.scores['recall'][cls],self.scores['f1'][cls]])\n",
    "       \n",
    "        for column in range(len(self.matrix)+1):\n",
    "            for row in range(len(self.classes)):   \n",
    "                ax.text(column,row,self.matrix[row][column],va='center',ha='center')\n",
    "\n",
    "        fig = plt.imshow(self.matrix, interpolation='nearest', cmap=self.cmap)\n",
    "        return ax\n",
    "\n",
    "\n",
    "    def show(self):\n",
    "        \"\"\"\n",
    "        Plots a classification report as a heatmap.\n",
    "        \"\"\"\n",
    "        plt.title('{} Classification Report'.format(self.name))\n",
    "        plt.colorbar()\n",
    "        x_tick_marks = np.arange(len(self.classes)+1)\n",
    "        y_tick_marks = np.arange(len(self.classes))\n",
    "        plt.xticks(x_tick_marks, ['precision', 'recall', 'f1-score'], rotation=45)\n",
    "        plt.yticks(y_tick_marks, self.classes)\n",
    "        plt.ylabel('Classes')\n",
    "        plt.xlabel('Measures')\n",
    "\n",
    "        return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "\n",
    "X = occupancy[['temp','humid','light','co2','hratio']]\n",
    "y = occupancy['occupied']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Throw an error if the model isn't a classifier!\n",
    "# model = LinearRegression()\n",
    "# model.fit(X,y)\n",
    "# y_pred = model.predict(X)\n",
    "# sviz = ROCAUC(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = LinearSVC()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fRA7vwoVzSEpai7y8XLRp44fRo6Ph7u6hdFlE0pM6oNmDJnJcFRUV2LVrOw4f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\nREREEvr/IP1yugbDm54AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b10ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sviz = ROCAUC(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h6OiE2bPnwNnZRZIsLGgiIiIAFy9eQGrqJhQXF8HDowmeeWaUZOUMsKCJiIhw4EAm0tN3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\nhW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b19de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)\n",
    "sviz = ROCAUC(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TC6F1in8niIjI1Z08+RM2bFiHy5fz0apVAAoLryAwMMiu+3CKgDZe/ceEQ9xERKSk8vIy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\nIiIJMaCJiIgkxIAmIiKSEAOaiIhIQv8fjlLJJZucjuQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b1b43c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)\n",
    "sviz = ROCAUC(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jjo5mxowZjlFOVj+bNm3i2WefpX379pQvX57169djt9vJzMykadOmpKam8txzz/HWW2/x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\niIiY4P/5KdR7D8CTxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b348c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)\n",
    "sviz = ClassificationReport(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o0aMJDw8vyupsjl3Po1ZZLeU9rT2YLnWd+eGiMd88l5JM1K+so8SvPblmvnp+\nvmrEbFFcSjTRurY1kLSOGl6oomf/ZSNx9yy4Oml4vqL1jK+SlyPF9BrOx5so4arhrRauuOisfUC/\n0loSM6yXGQ5dMdL2GSc0Gg1uTg4E+enZvn17geVdSjLT1FdPsV971YG+eg5ctq6DRUFWnsJsUeSa\nrG+yul87a+fvmNgTncuwiHuM35rO1bsPXvj3LT2QxT989DT00T+2PK3jQ4v9ZaJiU6hZwZ1y3tYT\ngk4B5dlzOiHfPJfvZFK/mhcl3K37rVntkvwcnYzZrLAoRY7RTG6eGWOehTyzBSeddb+fv5nOyav3\neGNJFKM/P2ntxT6mvCPRKZjNit2nEujRtCKuztYTjhGd/HjpeWvvxqIgIyMDgJw8M44OGhw01n24\n42gcbeuXpXmd0vnaful2pq0Xq3V0IKCmNwfO3bVN/+SbS7SpV9bWEwXIM1m4fCeTDQdvMmzRMaav\nO0diWs5D2+6jbTH0aFqRqmUenEx++eN1qpV1pcpvQv2vEnUtm1rlnGy9qM7Pe7DnQma+eS4l5FLf\nx4USbtbt3czPlSNXsqyv1YRcWj1jXTeto4YXqhXjQHQmeWZFSBMvnq9sPWZKumspXsyBpEwTeWbF\n5UQjG39JY+iqW0zflkBi+oPXyfVkIxt+SaN/Y6+H2rt161ayjBZeqPbgCk5cah6uTg74/1pXJW89\nxfQOnL+dQwlXR0a0LmnrcfuV1ZOUYa3rh3MZ9PyHJ65O1mlvtS5JqzrWdfnhfCYDm1nrL6Z3YG7v\nclTy1mO2KEa0LknlEtbXbuUS1u2Wnm3mQIyBgKrF8P21Z9yhrgevtyzxO/bKk61169bo9XqCg4OZ\nPXs248ePZ9WqVezduxdvb29iY2Pp2bMnQ4YMYcyYMWg0GiZNmsS8efMICQkhPDycqVOn4ubmxqhR\nowgJCaF///74+fnx4osvFlh3kXZBCro5XJSSMi2UcntwrlDSzYHsPOslzvuXfWuV0bL1ZA6JGWZK\nuzvy/fkcTBZIz1bULqvl3xdyqVNOi9EEP13OReuooaKXIzl5iqgbRhpU1nMx3sT1ZDPJBgt1Kzzo\nEeaZFZ8fNNC8hvWgT8qwUNr9QXtKuTlwNiGBitUeUV6KtbzaZbVsPpFNl7rOeDhr2HUhlxSDNaB7\nN3Bh1KY0ei9PJTtP0aWuM1VLasnJU1T2dqRvgAu1y+rYdymXCdsyWDmgOM6/Bv31ZBOHrxhZ/c8H\nbxaPKq9ueQcVAAAgAElEQVRayb9H7xQgKS2XUp4PegolPZzIzjWTnWu2XfatVdGDbUfiSEzLobSn\nMztPxGM2K9Kz82jzfFn2n02i79yfMVsUDap72Xp/HsV0tH6+DI1rleTc9TSmfnWWpa83fGR5JrOF\n9Ow84u5mk1ohjwmrT5OSaeTZyp681rYaAINaVWXsuh/4ZnMi6Vl5DGnni6er9U3vjY7WHvWJK6n5\n1q92JXd+OJVAnUqeGE0Wfjp3F62jdX99F3UHi1K0b1COiH03bMskZxipV604oa2rUaGECxt+usm0\niHMs+lcD2zxHY5JJSsul628uH0ddTuHM9TTCBzzH2JWn7LaPfq/EDBOl3B8cayXdtWQbLWQbLbYQ\nql3OmW3H00lMN1HaQ8v3ZzIwmRXp2WZqlXNm9/lM6lRwxmhSHIgxoHPUoHPU0O65B/fRvj2VTrZR\nUbucM8mZJupVdiH0RW8qeOnYcPQeU7fEs3hgRbKNFuZEJjG2Qymi7+Tma+vVJCNrv19LeJuSLNj9\n4ISnoreOnDzF8WtZ1K9SjOg7OVxPNpJiMFO30oPgzTMrlu9P4cWa1hOZW6l5pBrMTNh4h+RMM89W\ndGZIc2/uZZnJMlqIupbNjxeTMORaaPOsO90aWC9lN6/54ORo1U+pVPTWUcZTx62UPJx0GmbuSOBW\nSh5lPLQMbfG/F6gajYawsLB846pWrWr7f/r06Q8t4+vryxdffPHQ+C5dutClS5fCV66K0MSJE9X+\n/fttwy1atFBms/mx8xvvxtql3iVLlqhp06bZhk0mk6pVq5bKzs7ON9/GjRtVt27dVI8ePVRERIR6\n4YUX1L1791R6erqaOHGi6tSpk3r11VfV0qVL1bBhw5RSSh0/flz1799fvfzyy+q9995TQ4cOVbt2\n7bKVmZycrPr3769ef/11lZeXp5RSqk2bNurUqVO2eb7++mv11ltv/dfyli5dqjp37qx69+6tNm/e\nrOrXr6+UUmrMmDFq3rx5SimlkpKSVMeOHfO14bc6deqkjhw5YhuePHmyWrhwYb55/j/l/RX+6P78\n+OOP1dixY5XJZFIZGRkqJCRErVy58pF1DR06VG3evLnA8tq0aaMGDBigDAaDysvLU6NGjVKzZs1S\nSinVv39/tW7dOqWUUteuXVOBgYHq9OnT+ep499131YoVK2zDGRkZatKkSQ8db+fOnVPdu3dXOTk5\nSinr6+fs2bOP3U7169dXt27dsg0PGjRIbdmyxTYcFxenOnXqpO7evWtr686dOx9b3p+hKF+r9y1d\nulQ1a9ZMRUdHP7YdDRo0ULdu3VIjR460bbNNmzapoUOHKqWs+6hr167q8uXLSqmH9+HvfV941HEU\nHx+vatasqebMmaOUUuru3buqffv2avfu3fm203vvvafatGmj4uPjlVJKTZo0STVt2lTduHFDKaXU\n6tWr1csvv1yY3fBY1z9r94eWf5xr6177Q39/V0XaDSno5vCjxK/vb5d6nS7mcu1SLjcX7QQgId2M\nm16RtKKlbZ5so6JiloX5raw9nNS4+Vhy7pH+ZXsSM8z0K6FhaHsHIJn1P5/AO8vCzUVNSE8yMavx\n/c0WyaBv76Evf5ybl6ZyJcnElG8yCKyuZ8gzxbizzHp5wNuczvkvBuHla+2xXjqSRdnawQWWd/7M\nZOobFe3bWdt38cQsyhXL5uaiJuyKTGZ5/+LcXLQRgMbeWez+7B28opw5fNVoe4AKwJh8j5Ttb3Dz\nmA6LUny3LZXFfTy5uSjCNs/jyqt1yX6XBC0lnv3dy+quJ3D1XBLX18cB1vuCbs4OJGwbYZsnO9dM\neYORj3pb772lZu7EkpvJve/HELnxGG92rM6tjdZHwQLL32Pv5mUEaPaz/eht+vzm/mlW3BnuHbvD\nxYxvHlueB6k0LKElacdIABoVT+bL3fs4UzaGY8eO8sUXX3B9vfXxev/ysOuzsXg0ffDsQObVi6QY\nTnK92GEAEtNyCPbVMvhZ672urw9sxisnj9Vz3uRefArd2zZBKUhMMvDWkP681rYaZYo7ERtvoNVv\nHniy5GWTEDkBk4cTaYY8Th47yrgWcH19JAAbD94k8+4dBvRog1JwOyWbWZdOc/nfC+jYsPzv3j8A\nmoybv2s5fXQG16IN3Fj+MwAJaXm4OUHi2m62ebKNFioYzHzcwdrTT01ciSUnjbQNwSSmm+hbVsOQ\nKo7AHdYfWYlXlpkby9uTZ1Z88F0iN5Lz+KhbWZz3v8WN/daeZmxiLq3qWHuwSinMuZkkbPgnR/bH\nEXNsN8vmTSYjx0JWroUB7evR+lk3UuJSeOedd8i9e5nEdBMH/q0hbv8SBjT1Ji0xl5nN71+lOkho\n5E30Ppe4cf1DriTmMnVrAs1quDLkeW9ur+oMgIc5noauGdyN6AFAI6csvtyZSm/vfWgdFC+Yv+fG\n8j0ANPBOZv8XE6hx1ZvMHDPTtyWi0cBHXUqTu+Of3ACcbqVQyysPtXMIN4AX8izMuniNy0vaodfm\nfyBTFI0ivYdav35922PG/3lzuCg1qKzjYryJ2/esD118cyaXJtX0+eZJNlgYvSmdLKP1nuHao9m0\n/PVBnm/O5LLycDYAqQYLkWdzeKmWddqE7RnEJFjvgey7lIvOAaqV1BJ3z8w7m9MJecGFoYGuaDQP\nDuAmvjq+P5+D2aLIzLWwN8ZIq1atCiwvJsHEtG8zMFus9za/OpbNS7+2r0ZpLT/GWO+nZucpfrme\nxzPltDjrNKw8lE30r+UduWrEaFLUKmsN7Kt3zbg7ayjjkf8G6aPKq13273PJt0F1L6JvZXA7xbpP\nvv3lNo1rlcw3T3JGLu+sOEVWrnXdI/Zdp0Vd673K6uXd2HcuCQCT2cLP0cnUruSBi96RHUfj+Om8\n9fLd5TsZxNzOoGF17wLLC6xTin1nkzDmWVBKcfBCMjUreuBRTEcpD2e+//57wPow1ZlradSumP+h\nof/07S93+GLPVQBSM41ERsXTom5p/tW+OitGBLDoXw1Y/HoDvN2dGN+rFo1qlkCj0bA48jIJ96z3\nTbcfjaNaGVdKeFiPkXM30vCr4I6T7sG+7tm0EqtGPijPr7wbr7Wt9ofD9I9oUKUYF+/kcjs1D4Bv\nTmXQpHr+E7nkTBPvrL9NltF6y+PLw6m0qO326/zprPrJegk91WAi8nQ6L/16T3X6tgSyjYr5fctT\n2uPB8azRWB8sSkiz1rnjZDrVSjlR0k3Lun/5sHhgRZYMrMjApl48W9GZGT3K0rymG2uGVGbLli0s\nGViRxtWL0b2BJwOaWr9BZ+KmeGLirZeI90VnonXQULWUnrjUPMZ8fYf+TbwY2qJEvveFQD9X9kUb\nMJqsx9GhSwZqlnNC66ihka8r/z5rvRefbbQQdT2bmmWdyDMrxm2Ip1xxLeG9yuLm/GD/Nq3hyrm4\nHNt6HYgx4FNCL2H6J9IopdR/n+33Ub95yhcgPDw837Xs/3RzURO71X30mpHPD2ZhskB5TwfGtXXj\n9j0LH/6QyZK+1sfPt53KYdvpHFBQp7yW4UGu6LUaso2K2bsyiLtnfQH3/YeLLWzPxOXx6T4DJguU\ncHVg1EuulPFw5MMfMtlzMZeKXo7c36B6Rw0Le3titiiW/ZRF1I08TGZFp+eceXv5CW4uavJQeW+/\n5ErZXwNv5eEs64NICpr66hnUxAWNRkNCupkFew3Ep1tw0EALPz19A6yPzUddN/LZr+vtqtcwsqUr\nVX+9H7r/Ui7fns3l/W753+ALKs9e/kgPFeCXSyl8/u8rmM2Kct4ujO1ei9up2Xy8LcZ233D70Ti2\nH7mNUvCsjwdvdKyBXutAelYen357mct3MnF00FCvWnGGtPXF0VHDpdsZfPrtZbJyzWgdNQxr70vd\nKsULLM9iUUTsv8G+M4lYlDWwR3b2w8XJkasJmSw/6kDi9XM4ajR0bVyBdvXzP2U5b0s0PqWL2T42\nk51r5v3NF7idbA3HPi9WtoX3bw346AiTez9j+9jMntMJrNt/E6UUJT2cGNW1JqU8f/2oxk83uZWc\nzdsvP/4kdszKU7z8Qnm7fGzm9/ZQwfoxkOX7UjBbFOWK6xjXoRS375n4aGcSiwdWBKwfL9l2It26\nLyo68+ZLJX99rVp4/9tE4u5ZQ6RPIy9a1nbjXFwOo766TQUvnS1QNMDg5taPmOw5n8lXR1JRynrf\ndnS7Uvnu5QLsOpvBgRhDvo/NVB78HTeWt2fud4lUKam3fWzmzK1sFv2QjMmi8HbVMqptScp46vho\nZxJ7LmRS0UuX731hQf8KWJQi4vA9fryYiUVBjTJOjGxjfYApI8fMoh+SuZSQi0XBS8+40a+xF3vO\nZ/J+pLXu+9msAcZ1LE2VknoOXjKw5lAqZovC3dmRt9uWpJJ3/s7E/1flwd/9oeUf5f4VnN/Lp/cy\nO7XEvoo0UP+/7Bmof3eVXj/0P7W+fzRQnyQ+vZf94TeMJ80fCdQnyf1A/V8igVp48k1JQgghhB1I\noAohhBB2IIEqhBBC2IEEqhBCCGEHf5/PRgghhPifoPSef3UTioT0UIUQQgg7kEAVQggh7EACVQgh\nhLADCVQhhBDCDiRQhRBCCDuQQBVCCCHsQAJVCCGEsAMJVCGEEMIOJFCFEEIIO5BAFUIIIexAAlUI\nIYSwAwlUIYQQwg4kUIUQQgg7kF+bEUII8dRQSjFt2jSio6PR6/XMnDmTSpUq2aYvW7aMyMhI3N3d\nCQ0NJSgoiDt37jB27FgAPD09mTdvHk5OTuzZs4dFixah1Wrp0aMHvXr1KrBu6aEKIYR4auzevRuj\n0ci6desYPXo04eHhtmkxMTFERkayYcMGPv/8cxYsWEBubi6rVq2iQ4cOrFmzBl9fXzZu3IjJZGL2\n7NmsWrWKNWvWsH79epKTkwusWwJVCCHEUyMqKorAwEAA/P39OXv2rG1abGwsAQEB6HQ69Ho9Pj4+\nREdHU7t2bdLS0gAwGAxotVpiY2Px8fHBzc0NnU5HgwYNOHbsWIF1S6AKIYR4amRmZuLu7m4b1mq1\nWCwWAPz8/Dh27BhZWVmkpqZy4sQJsrKyKFOmDGvXrqVTp04cOHCAdu3aPVSOq6srGRkZBdYt91CF\nEEI8Ndzc3DAYDLZhi8WCg4O17+jr60vfvn0ZPHgw5cqVw9/fn+LFizNhwgTmzJlDkyZN2LdvH2PH\njmX06NFkZmbayjEYDHh4eBRYt/RQhRBCPDXq16/Pvn37ADh58iR+fn62aSkpKRgMBiIiIggLCyM+\nPp6aNWvi6emJm5sbAKVKlSI9PZ1q1apx/fp10tPTMRqN/PLLLzz//PMF1i09VCGEEE+N1q1bc/Dg\nQYKDgwEIDw9n1apV+Pj40KJFC2JjY+nZsyd6vZ4xY8ag0WiYNGkS06dPt10anjp1KlqtlvHjxzNo\n0CCUUvTq1YvSpUsXWLcEqhBCiKeGRqMhLCws37iqVava/p8+ffpDy/j6+vLFF188ND4oKIigoKBC\n1y2XfIUQQgg7kEAVQggh7EACVQghhLADCVQhhBDCDuShJCGEEH8uJ8+/ugVFQnqoQgghhB1IoAoh\nhBB2IIEqhBBC2IEEqhBCCGEHEqhCCCGEHUigCiGEEHYggSqEEELYQaEDNTExEYBjx47x5ZdfkpWV\nVWSNEkIIIZ40hQrUqVOnsnjxYi5fvszo0aM5d+4c48aNK+q2CSGEEE+MQgXqmTNnmDJlCt999x09\ne/Zk1qxZ3L59u6jbJoQQQjwxChWoZrMZi8XCDz/8wIsvvkh2djbZ2dlF3TYhhBDiiVGoQO3atSvN\nmjWjQoUK+Pv706NHD3r37l3UbRNCCCGeGIX6cvxXX32VgQMH4uBgzd+1a9fi7e1dpA0TQgghniSF\n6qHGxcURGhpKmzZtSExMZOTIkdy6dauo2yaEEEI8MQoVqFOmTCE0NJRixYpRqlQpOnXqJE/5CiGE\nEL9RqEBNTU2lWbNmAGg0Gl555RUyMzOLtGFCCCHEk6RQgers7Ex8fDwajQawfrmDXq8v0oYJIYQQ\nT5JCPZQ0fvx4hg4dyo0bN3j55ZdJS0tj/vz5Rd02IYQQ4olRqEB97rnn2LhxI9euXcNsNuPr64tO\npyvqtgkhhBBPjEIF6unTp4mKiqJfv34MGzaM8+fPExYWRtu2bYu6fUIIIUShKaWYNm0a0dHR6PV6\nZs6cSaVKlWzTly1bRmRkJO7u7gwePJjmzZsza9YsLly4gEajISkpCU9PT9atW8eMGTM4ceIErq6u\nACxatAg3N7fH1l2oQJ0xYwbvvPMOO3fuxNnZmc2bNzN8+HAJVCGEEH8ru3fvxmg0sm7dOk6dOkV4\neDiLFi0CICYmhsjISDZs2IBSiuDgYBo1asSECRMAMJlM9OvXjxkzZgBw/vx5Pv/8c4oXL16ougv1\nUJLFYiEgIIAff/yRNm3aUL58ecxm8+9ZVyGEEKLIREVFERgYCIC/vz9nz561TYuNjSUgIACdTode\nr8fHx4fo6Gjb9DVr1tC0aVOqV6+OUorr168zZcoU+vTpw6ZNm/5r3YUKVBcXF1asWMGRI0do0aIF\nq1evtnWBhRBCiL+LzMxM3N3dbcNarRaLxQKAn58fx44dIysri9TUVE6cOGH7KdK8vDzWr1/PoEGD\nAMjKyiIkJIQPPviA5cuXExERQUxMTIF1FypQ586dS1ZWFgsWLMDT05OEhATmzZv3u1ZWCCGEKCpu\nbm4YDAbbsMVisX1trq+vL3379mXw4MHMmDEDf39/vLy8ADh06BABAQG2e6QuLi6EhITg5OSEq6sr\njRo14uLFiwXWXah7qF5eXrRq1YpatWqxY8eOfA20p9w2q+1e5t/Z/9L6Ou0a8Fc34U/lkHz2v8/0\nFDH5Bf/VTfjTmKu0/6ub8MRTeo8iK7t+/frs3buXdu3acfLkSfz8/GzTUlJSMBgMREREkJmZSWho\nqG364cOHefHFF23zXr16lVGjRrF161ZMJhNRUVF07969wLoLFahjxoyhWrVq5ObmsnDhQl5++WXe\nffddVqxY8XvWVwghhCgSrVu35uDBgwQHW0/ywsPDWbVqFT4+PrRo0YLY2Fh69uyJXq9nzJgxti8s\nunbtGl27drWV4+vrS5cuXejVqxc6nY5u3brh6+tbYN2FCtRbt24xf/58PvjgA3r27MmQIUPo0aPH\n711fIYQQokhoNBrCwsLyjatatart/+nTpz9yuSVLljw0LjQ0lNDQ0ELXXegfGE9JSWH37t0EBQWR\nlJREbm5uoSsRQgghnnaF6qGGhobyyiuv0LJlS/z8/Gjbti1vvfVWUbdNCCGEeGIUKlA7d+5M586d\nbcORkZHk5eUVWaOEEEKIJ02hAnXnzp18+umnZGVloZTCYrGQnZ3Nzz//XNTtE0IIIZ4IhQrUDz74\ngBkzZrBy5UqGDRvGTz/9RGpqalG3TQghhHhiFOqhJA8PDxo1aoS/vz8ZGRkMHz6ckydPFnXbhBBC\niCdGoX9g/OrVq/j6+nL06FGMRiMZGRlF3TYhhBDiiVGoQB05ciQff/wxLVq04PDhwzRt2pSXXnqp\nqNsmhBBCPDEKdQ81ICCAgIAAADZt2kRaWhqenp5F2jAhhBDiSVJgoIaEhNi+lulRVq/+3/kuWiGE\nEKIgBQbq8OHDSUtLw2QyUaJECcD6a+jJycmULFnyT2mgEEII8SQo8B6qm5sbYWFhuLq62i77Hjp0\niPDwcDw8iu7XAoQQQognTYGB+v777zNv3rx8P2nz9ttvM2vWLGbPnl3kjRNCCCGeFAUGanp6Oi+8\n8MJD4wMDA+WLHYQQQojfKDBQTSYTFovlofEWi0W+y1cIIYT4jQID9R//+AeffPLJQ+MXLVrEs88+\nW2SNEkIIIZ40BT7lO2rUKIYMGcKOHTt47rnnUEpx/vx5vL29Wbx48Z/VRiGEEOJvr8BAdXNz48sv\nv+Tnn3/mwoULODg40K9fPxo2bPhntU8IIYR4IvzXb0rSaDQ0btyYxo0b/xntEUIIIZ5IhfouXyGE\nEEIUTAJVCCGEsAMJVCGEEMIOJFCFEEIIOyjUz7cJIYQQduNUdD//qZRi2rRpREdHo9frmTlzJpUq\nVbJNX7ZsGZGRkbi7uzN48GCaN2/OrFmzuHDhAhqNhqSkJDw9PVm3bh1ff/0169evR6fTMWzYMIKC\nggqsWwJVCCHEU2P37t0YjUbWrVvHqVOnCA8PZ9GiRQDExMQQGRnJhg0bUEoRHBxMo0aNmDBhAmD9\ndsB+/foxY8YM7t69y5o1a9iyZQs5OTn06dOHpk2botPpHlu3XPIVQgjx1IiKiiIwMBAAf39/zp49\na5sWGxtLQEAAOp0OvV6Pj48P0dHRtulr1qyhadOmVK9endOnT9OgQQO0Wi1ubm5UqVIl37yPIoEq\nhBDiqZGZmYm7u7ttWKvV2r6T3s/Pj2PHjpGVlUVqaionTpwgKysLgLy8PNavX8+gQYMeWU6xYsXI\nyMgosG655CuEEOKp4ebmhsFgsA1bLBYcHKx9R19fX/r27cvgwYMpV64c/v7+eHl5AXDo0CECAgJw\nc3OzlZOZmWkrx2Aw/NffAZceqhBCiKdG/fr12bdvHwAnT57Ez8/PNi0lJQWDwUBERARhYWHEx8fb\nph8+fDjfb3/XrVuXqKgojEYjGRkZXLlyhRo1ahRYt/RQhRBCPDVat27NwYMHCQ4OBiA8PJxVq1bh\n4+NDixYtiI2NpWfPnuj1esaMGYNGowHg2rVrdO3a1VZOyZIlCQkJoW/fviilGDVqFHq9vsC6JVCF\nEEI8NTQaDWFhYfnGVa1a1fb/9OnTH7nckiVLHhrXq1cvevXqVei65ZKvEEIIYQcSqEIIIYQdSKAK\nIYQQdiCBKoQQQtiBBKoQQghhBxKoQgghhB1IoAohhBB2IIEqhBBC2IEEqhBCCGEHEqhCCCGEHUig\nCiGEEHYggSqEEELYgQSqEEIIYQcSqEIIIYQdSKAKIYQQdiCBKoQQQtiBBKoQQghhBxKoQgghhB1o\n/+oGCCGE+N+i9J5/dROKhPRQhRBCCDuQQBVCCCHs4Km65Hv06FFWr15NXl4eVatW5a233sLFxSXf\nPNu3b+fbb7/FycmJSpUqMWzYMNzd3QHo06cPpUqVss3bvXt3goKCSEtL48MPPyQxMREHBwfefPNN\nateuDcCOHTv47rvv0Gg0lCtXjuHDh+Pp6YnFYmHx4sWcPXsWjUZDw4YNGTRokK2d/fr1o0SJEra6\n5syZg7OzM4cOHSIiIgIHBwfc3NwYPnw45cqVe2x5N27c4IMPPkCj0QBgNpu5fv06EydOpHHjxsyc\nOZNr167ZtkPdunUZPHhwvm0yY8YMSpYsybBhw/KNP378OCtXrmThwoX22D1/yM9Xjaw4lIXJDFVL\nOvJOKzdc9Jp882w5mc320zk4aTVU9nZkeJAr7s4OZORYmL/XQGySCRedhrbPOPOyvzPXU0zM+j6T\n+6WYLXAt2cy0Tu409dUDYDQpJu/IoNNzTgRWdwIg16SYtzuTy0lmUBDatJht/jNxeYzs0YPshHs4\naTW82dwVvzIPXmaZuRZGbUxnTGs3apS2jr9618SIr9OoUNzRNt+k9u5U9HJk66lsIo5m4+1qPfct\nptfwYU/r5bLvzuWw8XgOZouifmUdbzR3xdFBU2D79l3KJeJoNgAeLg6MbOmar96/wpGz11i1/WdM\nJgtVK5Tg7X4tcHHW55tn24+n2bH/DE56LZXLePFG7+a4FXNixvLvuXM3zTqTgvjkdOrWqMDUoR04\nFXOL5VsOYzabcdLrGNarGTV9ytjKNOaZmbbkWzoE1qHZ874A5OTm8eHaPdyIT0EpaNOoFj1a1QMg\nKTWD94cN48blM1gsisHdmtCgdmUA3vvsO67dTsbZSQeAf40KDOnRDIAd+8+w89AFjHkmqlcqxaiQ\nlmgdHbl2O5m3526ifOkHlz8nDGpLhdLFuXg1nkUbDpCTm0eJ4q6MHdgaL49iALw5+2vyTGa0Wusx\n0bKhHz1a1Suw7eLPUeSBeurUKebOncuaNWuKtJ60tDTmz5/P3LlzKVeuHCtXrmTlypW8/vrr+dqy\nefNmPvzwQ7y9vdmzZw8LFy5kwoQJxMXF4eHhwYIFCx4qe/HixdSpU4ewsDCuXLnCtGnTWL58OTdu\n3GDLli18+umnuLi48Pnnn7N27VreeOMN9uzZQ1xcHIsXL8ZsNvPOO+9w8OBBmjZtyoULFwgNDaVl\ny5b56jEajcybN49PP/2UsmXLsnXrVpYuXcq0adMKLO+3gbd8+XKqVq1K48aNAYiOjubjjz/G29v7\nkdtt48aNXLhwgcDAwHzt+L/27jwsqrJ94Ph3hkUQEEVxTVFARHE3NTQSNHPJ9VVzSfxVmoqVluaS\nmaktYKZlpqXmKy1uuaS5K2nuqZgYbiiEhriADNvAwMDM8/tjYoo35e2VUUzvz3V15cw553nuOYcz\n93mWc2bNmjVs3ry52AVGWck0mPlwt55PBrpT092OpQdzWHooh7EhrtZ1YpIK+PZEHp8Ocqeyi5ao\n8/l89EMO0592Y9H+XJwdNCwfVolCk2L6lmyqV9DStp4ji4dUtJax+EAOPp521uRz9loBn+zN4Uq6\niR5NylnX++qnXMo7aPh3aEVSsk2M/TaLBtXsqeKqZfYuPXMWTqZG9KscSjDywW49Xwy11HE00chn\n+3O4kW0u9vnOXCukU4NyvNrJlf905mohYU+4ENKgXLH3L6UV8tVPBhY/604FJy3vbc9m/ck8nmnl\nzJe3ic9OA/P35LB0aEUqu2jZdCqPBT/mENGnQukP0h3K1Bv46Js9fPR6P2pUceffG4+wbNMRXh7Y\nwbrOqQtXWBd1ko8n9qeyuws/HIvj45V7mTaiK9NGdLWud+FyCu8t28HLAztQaDIR8e9dvPdKL7xr\nVeHo6UvM+TKKL6Y/C8C5xOt8umYfV25k0D0owFrGuqiTlHO05/M3B5ObZ2TUu6to6leL+nWq8vZn\nW90VsSgAACAASURBVHlu1Dja9G9EQlIqUxZsYlX489jb2XE+8QYLJg/Aw92l2Oc7GJPA5n2xfPR6\nP1ycLRcA3+05xYDOLTn763VCWvsxdnBwsW0KTSbeW7aTqcO70LBedbYeOM28b/bwzpge5BkLuJ6W\nxZrZL2CnLd7BWFLs4t64q12+X3zxBdOmTaOgoOBuVgPAyZMn8fPzo0aNGgA8/fTT/Pjjj8XWSUhI\noHnz5tbk0q5dO44dO4bJZOLs2bNoNBreeOMNXn75ZVatWoVSCpPJxLFjx+ja1XLient7U6tWLU6c\nOIGvry9Lly7F2dkZo9FIWloaFSpYvpzMZjP5+fnk5+djNBopKCjA0dHyRX3u3Dl++uknxo0bx+TJ\nkzl9+rR1G4CcnBwA8vLyrNvcqjwHB4din+/06dMcPnyYl156CYAbN26Qm5vLwoULefnll/n444/J\nzs62rv/LL7/w888/061bt2LlnDhxgvz8fF577bVSHBHbib5cgH91e2q6W1pSvZo68cN5Y7F1LqYW\n0rKOA5V/b8k97uPIT4lGTGbFxZRCOje0JCR7Ow1t6zqyP7749rHJBRyINzLuT0l646k8XmhXHv/q\nxa87DyYY6d7YCYCqbna0qu3Avov5AJiV5eIOIMeocPxT42/TqTwmd3G1xljk7LVCfks38fLqTF5e\nk8nB+Pxiy/bE5TN6ZQZvbMwi8WYhAIcTjLTzcaSCk6WsHk2c+OG8ZbtDt4mvkouWtS9WorKLFpNZ\ncSPbhLtT8Vb+vfbzuSQaeFWjRhVLK+3poAD2Hr9QbJ3431Jp0eARKv+erNo39+Zo7CVMpj8uTApN\nJj78KorR/YOoXNEFezs7vnn/ObxrVUEpxbXUTCq4/NFb9f2Pv/Bcz8fwr1utWF1mpTDkFWAymzEW\nFKIU2NvZ8euVm+hz8xk4cCAAPrU9mTv+X2g0Gq6nZWHIN7Jg9T7C3l/NvK9/QJ9rORZ7jsXRr1Nz\nXJwtf3+vDOpAxzYNADj763WSrqcz7oO1vDpnHYdifgUsFwYuzo40rFcdgC7tGhITd4Xs3DziLqXg\n5OjAW4u2MPq91SxefxBjganE2MW9c1dbqF5eXixcuJBJkybdzWoASE1NpUqVKtbXlStXxmAwYDAY\nrN2dDRo0YPPmzaSmpuLp6cnu3bsxmUxkZWVhNptp0aIFI0aMID8/n7fffhsXFxdry60oURaVffPm\nTQDs7Ow4cuQIn3zyCY6OjoSGhgLw5JNPcvDgQYYNG2Ytu3Xr1taynn32Wby8vDh79izvvPMOn376\nKZUrV+all15iwoQJVKhQAbPZzJw5c25bXps2bYrtg+XLlzNs2DDr583IyKBFixa89NJLuLu7s3jx\nYubPn8+0adNIS0tj6dKlzJo1i+3btxcrJzAwkMDAQGJjY212fEojVW/G0/WPJFTFVYuhQGEwKmu3\nr381ezbG5JGSbaKqmx07zuZRaIYsg6JhdXt2n8snoIY9xkI4GJ+PvV3xRLLkYC4vtCtfrBt5alfL\nUMCaE4bi8WSb8XT7UzxuWm7qLV/uE550YdKkSbho88gxKmb3dbOu9/5tWoLODtCxQTl6NHHiN52J\n8esyqVbBjtqV7KjjYceQNs40rG5JilM3ZbN8WEVS9GZqVPjjy9LTVUvq7zGUFJ+dVsOFG4VM+z4L\nownC+7hRllLT9XhW+uMipkolVwx5BRjyjNZu3wZ1q7FpXyyp6dl4VnJj1+FzmExmsnLyrN2gOw6d\no0pFVwKb1rOWZafVkpGdy8sR35KVk8cbL3SxLpv8/FMArN19slg8A55swcSPN/Ls1EgMeQX0eKIx\n9WpVZv/P8VT1cCMiIoIje7dhZ6dlaPfW1KnuQWa2gRb+tXllUAfcXZ35bO0B5n2zh+kju3HlRgZ+\nXgamLdyMLjOHAJ+ajOjbDgDncg4EP1qfp4Mak3Q9nYkff0dVDzfLPqn4xz6xt7OjopszaRk5GPKN\nNG9Qi5cHdsDOTsvs5btZvukIo/o/ftvYHzZKKWbMmEFcXByOjo6899571K5d27p8yZIlbNu2DTc3\nN4YPH05wcDAGg4EZM2aQnJxMQUEB06ZNo0mTJkRGRrJu3TprI2zWrFnUrVu3xMrvqitXrqiBAwf+\nrXXz8vLuuJ7PP/9czZgxw/q6sLBQ+fv7K4PBUGy9devWqb59+6p+/fqplStXqrZt26qMjIy/lLdz\n5041dOhQdePGDdW0adNiyyZMmKBWrFjxl22+/fZb1alTJ6WUUh9//LGaNGmSKiwsVNnZ2So0NFQt\nX778lrGPHj1abdiwQcXFxanOnTurpKQkpZRSX331lerVq9ffKu/EiROqc+fOJe6j9PR0FRAQoPLz\n89XQoUPVTz/9pJRSasGCBeqdd975y/pHjx5VPXr0KLHMe6G0xzYrK0u9+eabqkePHur5559Xixcv\nVqNHj7Zu99/23dChQ9XOnTutrxs3bqzS0tKsr+fNm6c++OADdfPmTRUSEqLOnDmjlFJq9+7dKigo\n6C9xhoSEqNOnT9+2vnfeeUctWLDglst69uypjh49qt566y21dOlS6/uXLl1SgYGBSimlAgICbhnf\nf9q/f79q06aNys7Ovm0sd5utztunnnpKHT9+/Lb1nDlzRrVu3VpdunSp2Pv/eWwnTpyo5s6dq5RS\nKjU1VT399NNq165d6vvvv1f+/v5qz549SimlTp06pdq0aaNSUlL+UlfReVZQUKCeeuopNWzYMJWT\nk6MKCgrU+PHj1fvvv3/LGIuO++bNm9WLL75YbFlQUJCKj4//yzanT59WISEhJcZeGr/unl+q7W8n\n4afvSvVfSXbt2qWmTJmilFIqJiZGhYWFWZfFxcWp3r17K6PRqPLz81Xfvn1VXl6eWrBggfriiy+U\nUkqdP39ebdq0SSml1Ouvv249n/+O+2pSUlJS0h1vq9Vq+fXXX4mPjwcgJSUFV1dXrly5Yl3HYDBQ\ntWpVPvjgA8DSgjOZTKSmprJmzRq8vb2tVx/Xrl2joKCA9PR0zGYzMTExuLparhovX75MixYtOHDg\nAOnp6TRq1AiAZs2acfXqVU6ePMnWrVsJCwsjMTERsHQv//DDD7Rs2ZItW7YwdepUa6x6vZ60tDQ2\nbtxI/fr1ycvLIz4+nrZt2xIeHl5ieY8/bpn4sHr1aoKCgqxlApw5cwa9Xk/btm0BS1ekRqNh586d\nJCYmMnPmTJRSpKeno5QiJSWFsWPHWrdPTk7GaDQWK/NOlds17M63PZ/PpYv5JC3aCcCNLBOujorU\nf/8xBm0wKh7JNTP/SUurLT15Pua8DLJWdCMl28SzlTWM6qYF0ljz00k8cs0kLbK0FNbuyyGkhsb6\n+j/lX83k5o6zJMXPAMCzfCG/LOiKj+fvk4r26/H1tGPHu6uposmjUaNGJC1qRwNAY0jncMQT1glI\nAKbsdG6sfZ4K++wxK8Xq4wb6tnDG2cHSOs76JQeH8hqiI77mSKKRPs3+6KrMv5mB7vuXKH+lgIQ4\nRZLx3wCcTi7Awy6HpEXtqFredMv4YuasJ/FmIY96WVp+dQEncxbHPuxYLL47Ueg36I62s0+LI/F0\nAolRlrkLN9KycHV25NrBJdZ1DHlGqhsNzB0TDEBG9mXMBQZ0x79EByQkpZKv11E54zCJUYcByDEY\nOXXhCu2aeQPgDHhVdeXA2o8wN/e2lp2XnsyNX7aSqD0PwK4d2/j8zUHWeNrWr8iutYtp39wHV2dH\nQkJCSIz6BDegqrsjP66ajZOjPXpDPo81sbSOM/UGNJj5be9C3ByMtKzjyI3DXwDQprZi5Y7t/Ppo\nedbsPEGfkGbWiUyZv8Vgl1EeHJJJij9tjcFkMpOhS8Nw9jtWf38VF2dHGvvWBODK5RQo0JMY9clt\nY/fVnLujY/NPdeLECWvPYrNmzaxDamAZ9mvTpo11uMzLy4vz589z8OBBunXrxvDhw3Fzc2P69OmA\n5Tt08eLFpKamEhwczMiRI0us+57cNqOUuut1tGzZkri4OK5duwbA9u3brYmkiE6nY8qUKeTm5gKw\natUqgoODAUuS/Oabb6xjlZs3b+aJJ57Azs6O1q1bs2PHDgASExNJSkqiSZMm6HQ6Zs+ebR2X3Lt3\nL15eXri5ueHr68uBAwcAKCws5OjRozRs2BAnJye2bNnC7t27AcsBvnDhAq1atcLHx4fY2FgyMjIA\nOHLkCNWqVbttef7+/tbPFhsbS7NmzYp9XoPBwOLFi9Hr9QBs2LCB9u3b07BhQyIjI/nkk09YsGAB\n3bt3JygoqFgyvZ+0quPA+euFXM2wjBVtic2nnXfxWaBpOWYmrM8i12j5W/vmmIGOv0/k2RKbz/Ij\nlm7b9Bwz207nWZcBnEouoEXt4uPRJWnn7cjW03kApGabiL5s5LF6jnhXsedSmolLly4BcO56AfmF\nikdKmEWr1Wg4nFjA1lhLeTeyTByMz+eJ+uVwctAQecRA3A3LuOnRRCPGQoV/dXsCvR05kmgk02BG\nKcXW2DzrZKp2PreOz1ioeG+7nquZlv0Yk1SAyQxeHmU3ztayYR3OX7rB1VTLuPO2g2d47E/dtgBp\nmblM+ngjuXmWce+V26Pp0Kq+dXls/FWaN3ik2DZ2Wg3zvtnD2V+vA3DpahpXUtL/Mmb6n3zreLL/\nZ8sFZF5+AdHnfqOhd3UaeVfH0cGOffv2AZB0PZ3rN7OoV6syefkFfLb2gHXcdF3USYJa+KLRaAhq\n7sOBn+N/H9NUHPklkQZe1dBoNPwUe4lth84AlguJQzG/8ngLX/zrViM7N59ziZbYdxw+S8N61XBx\nduRmhp4vvjuMsaAQk9nMd3tirPvilrH/Pg77MNHr9dY7NwDs7e2t81P8/PyIjo4mNzeX9PR0YmJi\nMBgMpKenk52dzbJlywgODmb27NmAZS7OzJkz+eqrrzhx4oT1+N+ORt3lbJecnMyECRNYvXr1f123\ntC2h6OhoIiMjMZlMVK9enQkTJnDt2jUWLFhgnb27ZcsWtmzZglKKgIAAwsLCcHBwID8/n88//5zz\n589jMpkICgqyjodmZGQwf/58bty4gVarZcSIETRv3hywJO7Nmzdjb2+Ph4cHY8aMoWrVqmRnZ/PZ\nZ5+RkJCAnZ0dzZs3Z/jw4djZ2REfH09kZCQ6nQ57e3tGjhxJ48aNAdi6dSubN2/GwcEBNzc3Ro8e\nTZ06dUosD6B///4sXry42K04AN999x07d+5EKUXdunUZO3YsLi7FZyKuXLmSrKysv9w2Exsby+ef\nf87ChQtLdVygdC1UgGOXjCw7lEuhGWq6a5ncxZWrGWbm/aDn899n6m46lcemX/JAQUBNe14JdsHR\nXoPBqIjYlU1yhuWkGtLauVhC7bkojeXDKlHF9dbXl6+vz6R3MyfrbTOGAsX8PZbbUswKhrb5o7z9\nF/NZHV8Tky6Bcg4aRgeVp1GN4sl66PJ03n7azdoqvJpp4uMfckjPNaOAYW2deaK+pbwTl40s/f1z\nuzhqGNfRBe8qlu12nc3j25/zMJvBv7o9r3VywcFOU2J8B+Pz+fqoAY0GXMtpGPOEC96epe+outMW\nKkD0mcv8e9MRCk1mani6M3HYk1y7mcnHK/eycIplEtDm/bFs3heLUhDgU4MxzzyBo4Plb3/hmv1U\ndi/PoK6PFiv3dPxVlmw4hMlsxsHejhd6B9K0fq1i60yev5GeHZpYb5tJ0WXz6Zp9XL+ZhVarJbiV\nr7XcS1fT+PfuX0m5Eg9oeK5XW2urdMOeGHYcOotSULemB68+G4KLcznMZsWqndHsi76IUuBbuwpj\nBwfj7OTItZuZfLLqR9KzDCilGNq9NUEtfQHLxKSFa/aRX1CIm4sTE4c9SVUPN5RS/HvTEcukLLOi\nmV8txjwThL2dXYmxl0a9J21/of3r0Y2l2t67bZ/bLouIiKB58+bWiaTBwcHFJqiuW7eODRs2UKNG\nDQoLCwkLC2PGjBlMnz6dRo0akZWVxZAhQ9iyZQt6vd7aM7ly5UoyMzMJCwu7bd13PaH+L2zRtfhP\n4evr+1B93tIm1H+S2mMO37b7+EFVmoT6T1LvybHWLtWHxT8toe7atYu9e/cSHh5OTEwMixYtYskS\nyxCCTqdj8+bN/N///R96vZ7hw4ezevVqPvjgAypUqEBYWBhRUVFs2bKFd999lx49erB9+3acnJwY\nN24c/fv354knnrht3ffVGKoQQghRGp07d+bQoUMMGmS5yAsPDycyMhIvLy9CQkJISEigf//+ODo6\nMnHiRDQaDaNHj2batGkMGjQIBwcHZs+ejaurK+PHjyc0NJRy5coRGBhYYjIFSahCCCEeIBqNhpkz\nZxZ7r169P8blZ82a9Zdt3N3db/lEuF69etGrV6+/Xbc8y1cIIYSwAUmoQgghhA1IQhVCCCFsQBKq\nEEIIYQOSUIUQQggbkIQqhBBC2IAkVCGEEMIGJKEKIYQQNiAJVQghhLABSahCCCGEDUhCFUIIIWxA\nnuUrhBDinlKO7mUdwl0hLVQhhBDCBiShCiGEEDYgCVUIIYSwAUmoQgghhA1IQhVCCCFsQBKqEEII\nYQOSUIUQQggbkIQqhBBC2IAkVCGEEMIGJKEKIYQQNiAJVQghhLABSahCCCEeGEop3n77bQYNGsSw\nYcNISkoqtnzJkiX06dOH0NBQfvzxRwAMBgOTJ09m6NChDBw4kNjYWAD27NlD//79GTRoEGvXrv2v\ndcvD8YUQQjwwoqKiMBqNrF69mlOnThEeHs6iRYsAuHDhAtu2bWPt2rUopRg0aBCBgYEsW7YMPz8/\nZs+eTVxcHHFxcTRs2JCIiAg2bNhAuXLlGDx4MB07dqRy5cq3rVtaqEIIIR4YJ06cICgoCIBmzZpx\n+vRp67KEhATatGmDg4MDjo6OeHl5cf78eQ4ePIi9vT3Dhw/ns88+4/HHHychIQEvLy9cXV1xcHCg\nVatWREdHl1i3JFQhhBAPDL1ej5ubm/W1vb09ZrMZAD8/P6Kjo8nNzSU9PZ2YmBgMBgPp6elkZ2ez\nbNkygoODmT179l/KcXFxITs7u8S6pctXCCHEA8PV1ZWcnBzra7PZjFZraTv6+PgwZMgQRowYQY0a\nNWjatCkVK1akUqVKdOzYEYCOHTvyxRdf4Obmhl6vt5aTk5NDhQoVSqxbWqhCCCEeGC1btmTfvn0A\nxMTE4OfnZ12m0+nIyclh5cqVzJw5k+vXr9OgQQNatGhh3ebYsWP4+vri7e3N5cuXycrKwmg0cvz4\ncZo3b15i3dJCFUII8cDo3Lkzhw4dYtCgQQCEh4cTGRmJl5cXISEhJCQk0L9/fxwdHZk4cSIajYbR\no0czbdo0Bg0ahIODA7Nnz8be3p433niDF154AaUUAwYMoGrVqiXWLQlVCCHEA0Oj0TBz5sxi79Wr\nV8/671mzZv1lG3d3dxYsWPCX94ODgwkODv7bdUuXrxBCCGEDklCFEEIIG5CEKoQQQtiAJFQhhBDC\nBiShCiGEEDYgCVUIIYSwAbltRgghxL1VruQnDv1TSQtVCCGEsAFJqEIIIYQNSEIVQgghbEASqhBC\nCGEDklCFEEIIG5CEKoQQQtiAJFQhhBDCBiShCiGEEDYgCVUIIYSwAUmoQgghhA1IQhVCCCFsQBKq\nEEIIYQOSUIUQQggbkIQqhBBC2IAkVCGEEMIGNEopVdZBCCGEeHgknD1Rqu19GrWyUSS2JS1UIYQQ\nwgbsyzoAIYQQwlaUUsyYMYO4uDgcHR157733qF27tnX5kiVL2LZtG25ubgwfPpzg4GAyMzPp0qUL\nfn5+AHTu3JnQ0FDeffddTp48iYuLCwCLFi3C1dX1tnVLQhVCCPHAiIqKwmg0snr1ak6dOkV4eDiL\nFi0C4MKFC2zbto21a9eilGLQoEEEBgZy9uxZevTowbRp04qVdfbsWZYtW0bFihX/Vt3S5SuEEOKB\nceLECYKCggBo1qwZp0+fti5LSEigTZs2ODg44OjoiJeXF3FxcZw+fZozZ84QGhrKq6++ys2bN1FK\ncfnyZaZPn87gwYNZv379f61bEqoQQogHhl6vx83Nzfra3t4es9kMgJ+fH9HR0eTm5pKens7JkyfJ\nzc3Fx8eHsWPH8vXXX9OpUydmzZqFwWAgNDSUOXPm8MUXX7By5UouXLhQYt3S5SuEEOKB4erqSk5O\njvW12WxGq7W0HX18fBgyZAgjRoygRo0aNGvWjEqVKtGkSROcnZ0By/jpggULcHZ2JjQ0lHLlylGu\nXDkee+wxzp8/bx1nvRVpoQohhHhgtGzZkn379gEQExNTLAHqdDpycnJYuXIlM2fO5Pr16/j5+TFt\n2jR27twJwOHDhwkICODXX39lyJAhKKUoKCjgxIkTBAQElFi33IcqhBDinrqb96H+eZYvQHh4OPv2\n7cPLy4uQkBCmT5/O2bNncXR0ZPz48Tz66KNcuXKFqVOnAlC+fHneffddqlSpwrJly9i+fTsODg70\n6dOHgQMHlhiXJFQh7rL4+Hjq1q2Lvb2MsDzIlFJoNBrr6z93NYri4uPjS7W9r6+vjSKxLTna9zGT\nyVTWIYhS2r59O5999hlnzpyhsLCwrMMRd4nZbEaj0ZCdnc21a9fQ6/VotVqkvfJwkYR6nzKbzdjZ\n2aGU4tSpU1y/fr2sQxL/A6UUH330EZ06daJp06Zs2rRJkuoDTKvVcuPGDYYPH86qVavo378/CQkJ\naDQaSaoPEUmo9yGTyWS9uh07diwREREsXbqUqKiosg5N/E0ajYa4uDgmTJjA4MGDqVOnDhs3bpSk\n+oApuh3DaDQSHh7OmDFjGDFiBCaTiVWrVpGTk1OsG1g82CSh3oeKWqbLli2jVatWREZG0qBBA2Ji\nYqwz0cT9q6CgAIDPP/8cNzc3xo4dy5AhQ/Dy8mLz5s3ExMRId/4DoGiMND09Ha1WS8OGDbl48SJj\nx45l+fLl1KtXj+jo6LIOU9xDklDvI0VXuwDR0dGsWbPGeg9Up06dqFGjBidOnCAtLa0MoxQlMZvN\nODg4oNPpSE5O5v3338fLy4tXXnmFIUOG4OnpyQ8//CCt1AeAVqtFp9MxadIkoqKicHJyYufOnQQF\nBZGSksKaNWvw8fEp6zDFPSTTDu8TJpPJ2jKNi4vDz8+PyZMns3LlSurXr8+jjz5Kt27dyM/Pp3Ll\nymUdrrgFpRRarZaUlBRGjx6Nr68vhYWFzJs3j4iICJ577jkiIyPJzc2lXLlyZR2uKCWj0cj06dNx\nd3ena9eupKenYzAYMBgMLF26lI8++ohHHnmkrMMU95DdjBkzZpR1EMJytWs2mxk1ahRxcXEsWLCA\nDh06UKdOHRYvXswjjzyCn59fsUdqiftL0SzPyZMnM3z4cJ588kk2bdpEbGwsb775JklJSfj4+ODp\n6VnWoYo7VDS/oaCgAEdHR6pUqcKPP/6Iq6srAQEBNGvWjMDAQDp06ED16tXLOtz7lk6nK9X2Hh4e\nNorEtiShlrGkpCRcXFzQarVERERQuXJlZsyYgb+/P3PnziU0NJRatWpRpUoVOUHvU0VfsmAZPzWb\nzVSrVo0VK1bQs2dPoqKiOHbsGG+//bZcEP2DFc28v379OrNmzWLjxo3UrVsXb29v1q9fj6OjI/Xr\n10er1eLo6CiTkUrwoCZU6fItQ0eOHEGv11t/q69GjRo4ODgA0Lp1azp27EhsbCx9+vQpyzBFCYq+\nZG/cuMHevXvx8vLCz8+PgwcP0q9fP8xmM35+fowcObKsQxWlVDQB6c0336RPnz54eXmxZMkSunfv\nzvDhw/nmm2/o0KEDLi4ukkwfUpJQy1BgYCAAX375Jb6+vpQvX57k5GSioqKoUKECu3btQjoQ7m9a\nrZbU1FQmTpxIQEAAjRo1omnTpuzcuZPNmzdz9OhRFi5cSJ06dco6VHGH/vzEo+TkZJydnenZsycA\nYWFhRERE8PXXX9OyZUvKly9flqGKMiYJtQwUTUAqkpuby8GDB2nfvj3p6enExsZy7tw5Jk+eTMuW\nLcswUlGSoi/aDRs2EBAQwOTJkwE4duwYrq6uBAUFMXLkSGrVqlXGkYo79edbY3Q6HSaTidq1a3Pk\nyBECAwPR6XS4uLhgMBgkmQpJqPdaQUEBDg4OmM1mZsyYQaNGjQgLCyMyMpLjx4/Ttm1b2rVrR1ZW\nFhUqVCjrcMUtFH3JFj0Bp3bt2mi1WnJzcylfvjw///wzNWvWpF27dmUcqSitP8/afvzxx2natCme\nnp4cOXKEb7/9lvT0dKZOnWr96S/xcJOEeo8VjZGOHTuW1q1bU7NmTXbt2kX79u3ZunUrW7ZsoXHj\nxjJ55T5VlExTUlJYsWIFDRo0IC8vj6NHj+Lg4EBubi4//fSTdNU/AIp+tmv27Nn07duX0NBQAJYu\nXUrLli3p2LEj1apVo0aNGmUcqbhfSEK9RzZu3IjBYGDw4MFcv34dvV5Phw4diIiIoHbt2mzdupUP\nPviAtLQ0aZnep4qSqU6n48UXX+T5559nx44deHl50atXL8xmM3q9npkzZ1KvXr2yDlfcoaIhGY1G\ng6OjI/Xq1aNSpUrW46/X66lTpw7e3t5lHaq4z0hCvUeKZurOmzeP8ePH07RpU44cOcKoUaOoX78+\nYWFh6HQ6atasWcaRilsp+jLNyMggLi6O/v3706dPH9asWUOzZs3w9PS0TjIT/1xFyfTGjRt8//33\n9OvXDw8PD86cOYODgwN5eXkcOHCAwYMHl3Wo4j4kjx68y/7zma0XL15kzJgx1qS6d+9ehg0bxogR\nI6Tr6D5WNDFl5MiRnDlzhiVLltCzZ08WLVqEt7c3K1asICcnR35Z5B/Ozs6OlJQUpkyZglKKCxcu\n8NRTT+Hi4kJSUhI//PADc+fOlXvCxS1JC/UuKrpH0Ww2895771GpUiXmz5/P1KlTGTlyJEuWLEGj\n0dCtWzcaNmxY1uGKEpjNZnbs2EFhYSFPPvkk7u7ufPbZZ0RFRbFixQo+/PBDXFxcyjpMYQMbvC8i\nzQAACE9JREFUN27Ex8eHrl278tZbb9G6dWuqVavGgAEDMBgMMgFJ3JZGySX1XRcWFkb9+vUJCgqi\ndevW5OXlMXnyZHQ6HV9//XVZhyf+Jp1Ox7fffktGRga9e/cG4NKlSwQEBMh9pg+QdevWkZycTHJy\nMiNGjGDPnj3k5+czbtw4lFLy0AYbiI+PL9X2vr6+NorEtqSFehf8+aS7du0aGo2G8ePHA5Yu36VL\nlzJ//nzi4uLKMkzxP/Lw8OCZZ57h+++/Z9WqVTz//PN069atrMMSpVQ0Pl70/8cee4wKFSrwyy+/\ncPHiRY4ePcr06dMBJJmKEskYqo2ZTKZiJ13VqlVxdXVl3rx5ADg5OZGVlUVGRgYNGjQoqzDFHfLw\n8KBnz57Ur19fZmP/wy1fvpzMzEy0Wq31eczR0dF8+umnpKenk5WVRUJCAlOnTpVZ2+JvkS5fGypq\nmZrNZsaOHYu3tzfx8fGMGjWKVatWkZWVRWpqKmFhYXTs2LGswxWl8J9PuxL/LHq9nhdffJHmzZsz\natQoKlasSGpqKq+//jrPPfccISEhANLFe5dIl68o0Z9PvEmTJhEYGEi3bt145plniIqKYubMmVy8\neBFXV1fq1q1btsGKUpNk+s9kNptZvnw53t7e5OfnY29vz9y5c5kwYQKenp6Eh4dTs2ZN6/ksyVT8\nL6TL1wbMZnOxE69u3br4+Pjw1ltvMXXqVLy8vEhMTKRx48aSTIUoQ/Pnz+fkyZO0aNGCQYMGMXTo\nUDw8PJg3bx4ZGRnUrFnzL+ezEH+XJNRSUkpZn+s6depUNm7ciE6nY/bs2bRr146AgAC++uoruT9R\niPtA7969+e2335gyZQoBAQFUq1aNvn374unpybvvvmsdUxXiTkiXbyn8eRzt9ddfx9nZmT59+tCk\nSRNu3ryJXq9nwoQJTJgwQe4zFeI+4OXlhb29PfHx8dYfufby8qJ79+7s3r0bo9FYxhE+HO7XMdDS\nkklJd+jPU+2PHTvGxx9/TGFhIXPnzsXLy4tr167h5OSETqfDx8enrMMVQvxOp9ORlJREeHg4zz//\nPF26dAH++CUoIe6U3Qz5WYz/2Z+7eceMGcPVq1cxmUwcO3aMmJgYAgMDqVWrFs7Oznh4eJR1uEKI\nP3F2dqZ69epUrVrV+hhBb29vmWgmSk1aqKWwcOFCrly5Qnh4OEopXn31Vfbv30+tWrVYt24dTk5O\nZR2iEKIER44coXbt2jzyyCNlHYp4AMgY6h3Kzs4mLy+PtLQ0zp8/j7+/Pz179qRr1640btxYkqkQ\n/wDyC0HClqSFWgqZmZls2LCBK1eu4O3tzaZNmxg3bhzt27cv69CEEELcYzI/vBTc3d3p3bs3lSpV\nYtu2bQwYMID27dvLLTJCCPEQki7fUvLw8GDo0KGUL1+euLg4a/evEEKIh4u0UG2gYsWK9O7dm7p1\n61KlSpWyDkcIIUQZkDFUG5IHpgshxMNLEqoQQghhA9LlK4QQQtiAJFQhhBDCBiShCiGEEDYgCVU8\nlJKTk/H39+ftt98u9v65c+fw9/dn48aNZRSZEOKfShKqeGhVrFiRAwcOFHsQx7Zt26hcuXIZRiWE\n+KeShCoeWuXLl6dRo0YcP37c+t6hQ4esz3c9cOAAAwYM4F//+hdjx44lMzMTgO3btzNw4ED69OlD\n165diY6OBmD58uX07t2bf/3rX9aW73fffccbb7xhLT80NJTjx49z7NgxBgwYQL9+/XjjjTfIzc1l\nypQp9OvXj759+7Jt2zYA4uLiGDhwIP379+fZZ5/lt99+uyf7Rgjxv5MnJYmHWrdu3dixYwdt2rQh\nNjYWf39/lFKkpaURGRnJ119/jZubG2vWrGHOnDm88847fPvttyxevJiKFSuyfv16li1bRsuWLVmy\nZAkHDx5Eq9Uya9YsUlJSSqz78uXL7N27FxcXF+bOnUvjxo2JiIhAr9czePBgmjRpQmRkJC+88AJd\nunRh+/btxMTEUKdOnXu0d4QQ/wtJqOKhpdFo6NixIx999BFg6e7t3r07W7duxcnJiWvXrjFs2DCU\nUpjNZipWrIhGo2HBggXs3buXxMREjh07hp2dHVqtlpYtW9KvXz86derEs88+S9WqVUusv169eri4\nuABw+PBh8vPzWbduHQB5eXkkJCQQEhLCzJkz2b9/PyEhIXTt2vXu7hQhxB2ThCoeas7OzjRs2JDo\n6GiOHj3KxIkT2bp1KyaTiVatWrFo0SIAjEYjubm55ObmMmDAAHr37k3r1q1p0KABK1asACy/j3vq\n1Cn279/P8OHDmTt3LhqNplh9hYWF1n+XK1fO+m+z2cycOXNo2LAhAGlpaVSsWBE7OzuaN2/Ojz/+\nyJdffsm+fft455137vZuEULcARlDFQ+9rl278uGHH9K4cWO0WsspkZeXR0xMDJcuXQIsyXL27Nlc\nunQJrVbL6NGjeeyxx9i/fz9msxmdTkf37t3x8/PjlVdeoX379sTFxVGpUiUSEhIASEpKIi4u7pYx\nPPbYY6xcuRKAlJQUevXqxdWrV3nttdf45ZdfeOaZZxg3bhxnz569+ztECHFHpIUqHnohISFMmzaN\n1157zfpe1apVef/993n11Vcxm81Ur16dOXPm4Obmhr+/P126dKF8+fK0bt2aq1ev4uHhwTPPPEO/\nfv1wdnamZs2a9O3bF3t7e9avX0/Xrl3x9vbm0UcfvWUML730EjNnzqRnz56YzWYmTZpE7dq1GTVq\nFNOmTWPRokXY29sXm+AkhLi/yLN8hRBCCBuQLl8hhBDCBiShCiGEEDYgCVUIIYSwAUmoQgghhA1I\nQhVCCCFsQBKqEEIIYQOSUIUQQggb+H/MMl1QU7UHBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b348c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)\n",
    "sviz = ClassificationReport(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ePXsyePBgRo4ciUqlYuzYscyaNYvw8HCioqIYP348bm5uDBs2jPDwcPr160dA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\nhBB2IIEqhBBC2IEEqhBCCGEH/w94zwpUKIR22AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b547dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearSVC()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)\n",
    "sviz = ClassificationReport(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from yellowbrick.bestfit import draw_best_fit\n",
    "\n",
    "class RegressionScoreVisualizer(ScoreVisualizer):\n",
    "    def __init__(self, model):\n",
    "        \"\"\"\n",
    "        Check to see if model is an instance of a regressor.\n",
    "        Should return a metrics mismatch error if it isn't.\n",
    "        \"\"\"\n",
    "        pass\n",
    "    \n",
    "class PredictionError(RegressionScoreVisualizer):\n",
    "    \"\"\"\n",
    "    Plot the actual targets from the dataset against the \n",
    "    predicted values generated by our model(s).\n",
    "    \"\"\"\n",
    "    def __init__(self, model, **kwargs):\n",
    "        self.estimator = model\n",
    "        self.name = get_model_name(self.estimator)\n",
    "        self.colors = {\n",
    "            'point': kwargs.pop('point_color', '#F2BE2C'),\n",
    "            'line': kwargs.pop('line_color', '#2B94E9'),\n",
    "        }\n",
    "        self.fig, self.ax = plt.subplots()\n",
    "        \n",
    "    def score(self, y, y_pred=None, **kwargs):\n",
    "        \"\"\"\n",
    "        Originally score  for prediction error was conceived as generating\n",
    "        y_pred by calling the sklearn function cross_val_predict on the \n",
    "        model, X, y, and the specified number of folds, e.g.:\n",
    "        \n",
    "            y_pred = cv.cross_val_predict(model, X, y, cv=12)\n",
    "        \n",
    "        With the new API, there's not much for score to do.\n",
    "        \n",
    "        \"\"\"\n",
    "        self.draw(y,y_pred)\n",
    "        \n",
    "    def draw(self, y, y_pred):\n",
    "        \"\"\"\n",
    "        If score is happening inside a loop, draw would get called multiple times.\n",
    "        \n",
    "        But, ideally we'd want the best fit line to be drawn only once?\n",
    "        \"\"\"\n",
    "        self.ax.scatter(y, y_pred, c='#F2BE2C')\n",
    "        draw_best_fit(y, y_pred, self.ax, 'linear', ls='--', lw=2, c=self.colors['line'])\n",
    "        return self.ax\n",
    "    \n",
    "    def show(self):\n",
    "        self.ax.set_xlim(y.min()-1, y.max()+1)\n",
    "        self.ax.set_ylim(y_pred.min()-1, y_pred.max()+1)\n",
    "        self.ax.set_title('Prediction Error for {}'.format(self.name))\n",
    "        self.ax.set_ylabel('Predicted')\n",
    "        plt.xlabel('Measured')\n",
    "        return plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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4iUD3UBDCQ4FoBEEQBOEniDrT3rlzJ3bs2AGO49Dc3IxTp05h06ZNePnllyGTyTBy5Eg8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xAAAg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\n/gEWXexVkf7wxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3c8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, RANSACRegressor \n",
    "from sklearn import cross_validation as cv\n",
    "\n",
    "X = concrete[['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']]\n",
    "y = concrete['strength']\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X,y)\n",
    "sviz = PredictionError(model)\n",
    "y_pred = cv.cross_val_predict(model, X, y, cv=12)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAEe3vWi6ntI4cx/fvxDTU44hPy9T0PITBM20CYJoMXjjlrblWqcFZYSvIdEmiCCA9v26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\nJYg2AACWINoAAFiCaAMAYIlvL0deBkGSccgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b57f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Ridge()\n",
    "model.fit(X,y)\n",
    "sviz = PredictionError(model)\n",
    "y_pred = cv.cross_val_predict(model, X, y, cv=12)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QPoc3FjtqChQzJwiCIHyOlpbISWJOEARB+BwtLZHT5WL+22+/ITEx0eCzXbt2Ydq0abqf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2N8hVH6QiWuwi03x/AV2E5VRGHu4l04uP33LQocK8rPEvQKX/elPaROeXMIC7aU4aMCr8BSwOXD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\nERERg/sPUe+s4ggQR4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b789a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = RANSACRegressor()\n",
    "model.fit(X,y)\n",
    "sviz = PredictionError(model)\n",
    "y_pred = cv.cross_val_predict(model, X, y, cv=12)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class ResidualsPlot(RegressionScoreVisualizer):\n",
    "    def __init__(self, model, **kwargs):\n",
    "        self.estimator = model\n",
    "        self.name = get_model_name(self.estimator)\n",
    "        self.colors = {\n",
    "            'train_point': kwargs.pop('train_point_color', '#2B94E9'),\n",
    "            'test_point': kwargs.pop('test_point_color', '#94BA65'),\n",
    "            'line': kwargs.pop('line_color', '#333333'),\n",
    "        }\n",
    "        self.fig, self.ax = plt.subplots()\n",
    "        \n",
    "    def score(self, y, y_pred, y_test=None, y_test_pred=None):\n",
    "        \"\"\"\n",
    "        Is there a better way to differentiate between train and test points?\n",
    "        \n",
    "        We'd like to color them differently in draw...       \n",
    "        Can the user pass those in as keyword arguments?\n",
    "        \"\"\"\n",
    "        self.draw(y, y_pred, y_test, y_test_pred)\n",
    "        \n",
    "    def draw(self, y, y_pred, y_test, y_test_pred):\n",
    "        self.ax.scatter(y_pred, y_pred - y, c=self.colors['train_point'], s=40, alpha=0.5)\n",
    "        if y_test is not None:\n",
    "            self.ax.scatter(y_test, y_test_pred - y_test, c=self.colors['test_point'], s=40)\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "        return self.ax\n",
    "\n",
    "    def show(self):\n",
    "        \"\"\"\n",
    "        The user calls show\n",
    "        \"\"\"\n",
    "        self.ax.hlines(y=0, xmin=0, xmax=100)\n",
    "        self.ax.set_title('Residuals for {} Model'.format(self.name))\n",
    "        self.ax.set_ylabel('Residuals')\n",
    "        plt.xlabel(\"Predicted Value\")\n",
    "        \n",
    "        return plt\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GXPAffPBBHnzwwR7lL7zwwlg3RTCCrFy8PrKGX3jaErYe9rBm8S3j2Kre6XVt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\ndIw2t451rtoSQl+KGJeyOGMjq28AszLdbjWbcPovsDj+JJr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KGlTRiCBpIBvBVF52TyIB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k1rxpWb7QK3ZxCVy8nF9jbCqkEURJGN9lG105Z8/YnVAFeroihzY3QZLLj9bgaCblRSiJA+B5na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\nvhBCCKECkvCFEEIIFfgf65b3AGZW4ocAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d9a8630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split as tts\n",
    "\n",
    "model = LinearRegression()\n",
    "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)\n",
    "model.fit(X_train,y_train)\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "sviz = ResidualsPlot(model)\n",
    "sviz.score(y_train, y_train_pred,y_test, y_test_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eG9mmGVRX5vHOKT+BNM+OdOxxnwlz5xlaugL9/AlEil2BYHCE4AsmBbGFxmdS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\n48UiJUFTenyuquI68fWtVXQfHKb9hkaefPhZoq53k57xEQ/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4o2D0b7nlFp555hlEcXnc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6a2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Ridge()\n",
    "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)\n",
    "model.fit(X_train,y_train)\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "sviz = ResidualsPlot(model)\n",
    "sviz.score(y_train, y_train_pred,y_test, y_test_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BP0KdzjaxQnwdCXshBtnmbW+SNbkj6Jtpa3Zz8aKJX/3UzqbSZ4HbJPBHGNkk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\nHRALIHkqMRevJplR8IViKJqeSEJP0aF+7tkpgr0gfFCJYC+cV2ca371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SSQw2F4moStA/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\n1I7xn4ebAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6d75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = RANSACRegressor()\n",
    "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)\n",
    "model.fit(X_train,y_train)\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "sviz = ResidualsPlot(model)\n",
    "sviz.score(y_train, y_train_pred,y_test, y_test_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from yellowbrick.yb_palettes import YELLOWBRICK_PALETTES\n",
    "\n",
    "class ClassBalance(ClassificationScoreVisualizer):\n",
    "    \"\"\"\n",
    "    Class balance chart that shows the support for each class in the\n",
    "    fitted classification model.\n",
    "    \"\"\"\n",
    "    def __init__(self, model, **kwargs):\n",
    "        \"\"\"\n",
    "        Pass in a fitted model to generate a class balance chart.\n",
    "        \"\"\"\n",
    "        self.estimator = model\n",
    "        self.name      = get_model_name(self.estimator)\n",
    "        self.colors    = kwargs.pop('colors', YELLOWBRICK_PALETTES['paired'])\n",
    "        self.classes   = model.classes_\n",
    "\n",
    "    def score(self, y, y_pred=None, **kwargs):\n",
    "        \"\"\"\n",
    "        Generates the Scikit-Learn precision_recall_fscore_support\n",
    "        \"\"\"\n",
    "        self.scores  = precision_recall_fscore_support(y, y_pred, labels=self.classes)\n",
    "        self.support = dict(zip(self.classes, self.scores[-1])  )\n",
    "        self.draw()\n",
    "\n",
    "    def draw(self):\n",
    "        \"\"\"\n",
    "        Renders the class balance chart across the axis.\n",
    "        \n",
    "        TODO: Would rather not have to set the colors with this method. \n",
    "        Refactor to make better use of yb_palettes module?\n",
    "        \n",
    "        \"\"\"\n",
    "        colors = self.colors[0:len(self.classes)]\n",
    "        plt.bar(np.arange(len(self.support)), self.support.values(), color=colors, align='center', width=0.5)\n",
    "\n",
    "    def show(self):\n",
    "        \"\"\"\n",
    "        Plots a class balance chart\n",
    "        \"\"\"\n",
    "        plt.xticks(np.arange(len(self.support)), self.support.keys())\n",
    "        cmax, cmin = max(self.support.values()), min(self.support.values())\n",
    "        ceiling = cmax + cmax*0.1\n",
    "        span = cmax - cmin\n",
    "        plt.ylim(0, ceiling)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = credit[['limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n",
    "    'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n",
    "    'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n",
    "    'jul_pay', 'aug_pay', 'sep_pay']]\n",
    "y = credit['default']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6d7860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearSVC()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)\n",
    "sviz = ClassBalance(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b7bebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = occupancy[['temp','humid','light','co2','hratio']]\n",
    "y = occupancy['occupied']\n",
    "\n",
    "model = LinearSVC()\n",
    "model.fit(X,y)\n",
    "y_pred = model.predict(X)\n",
    "sviz = ClassBalance(model)\n",
    "sviz.score(y,y_pred)\n",
    "sviz.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
